Keywords: Lesion Counting, Medical Instance Segmentation, Poisson-binomial Counting, Model Calibration, Robustness, Uncertainty, Multi-task Learning
TL;DR: This work introduces a novel continuously differentiable function that coherently maps lesion segmentation predictions to lesion count probability distributions suitable for both post hoc analysis and multi-task learning.
Abstract: Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
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Paper Type: methodological development
Primary Subject Area: Segmentation
Secondary Subject Area: Uncertainty Estimation
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Code And Data: https://github.com/SchroeterJulien/MIDL-2022-Segmentation-Consistent-Lesion-Counting
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2204.05276/code)